OD44B:
Artificial Intelligence Systems for Advancing the Study of Aquatic Ecosystems II Posters
Session ID#: 85898
Session Description:
Scientists studying aquatic ecosystems are increasingly able to collect big data; large and complex datasets necessitating more computing intensive analyses. While the data (e.g., from acoustics or omics) themselves can be quite different, the methods to analyze them are often rather similar. In many cases, artificial intelligence (AI; e.g., machine learning, deep learning) can expedite analyses by limiting the amount of human interaction needed. Furthermore, AI-based analyses are often able to detect patterns that traditional statistics do not pick up on. AI research has begun to surface in all corners of aquatic sciences. Researchers dealing with in situ imagery, and passive and active acoustic data have made particularly rapid progress, but other research areas are also pushing boundaries by applying AI techniques. Examples of such research include ocean -omics research and eDNA, autonomous sampling, fisheries research and management, as well as satellite imagery processing and the automated identification of sea surface features. We invite practitioners from various oceanographic disciplines to submit abstracts highlighting their research on big data and AI at all levels of biological organization (individual, population, ecosystems) and spatio-temporal scales. Given the nascent nature of this field, submissions that focus on methodological innovations are equally welcome to those delving into using AI to address ecological questions.
Co-Sponsor(s):
Primary Chair: Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Co-chairs: Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States, Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Primary Liaison: Moritz S Schmid, Oregon State University, Hatfield Marine Science Center, Newport, OR, United States
Moderators: Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Student Paper Review Liaisons: Christian Briseño-Avena, Oregon State University, Hatfield Marine Science Center, Newport, United States and Emlyn Davies, SINTEF Ocean, Trondheim, Norway
Abstracts Submitted to this Session:
An Open-Source System for Do-It-Yourself AI in the Marine Environment (642487)
Anthony Hoogs1, Matthew David Dawkins2, Benjamin Richards3, George Cutter4, Deborah Hart5, M. Elizabeth Clarke6, William Michaels7, Jon Crall8, Linus Sherrill8, Neil Siekierski9, Matthew Woehlke9 and Kyle Edwards9, (1)Kitware, Clifton Park, United States, (2)Kitware, Saratoga Springs, NY, United States, (3)NOAA, Honolulu, HI, United States, (4)NOAA Southwest Fisheries Science Center, Antarctic Ecosystem Research Division, La Jolla, CA, United States, (5)NOAA Fisheries Woods Hole Laboratory, Woods Hole, United States, (6)NOAA NWFSC, Seattle, WA, United States, (7)NOAA. Fisheries, US DOC, Silver Spring, MD, United States, (8)Kitware, NY, United States, (9)Kitware Inc., Clifton Park, United States
High-temporal resolution in situ imaging and machine learning to observe copepod-parasite interactions (654175)
Eric Coughlin Orenstein1, Christian Briseño-Avena2, Paul Roberts1, Jules S Jaffe3 and Peter J. S. Franks4, (1)Monterey Bay Aquarium Research Institute, Moss Landing, United States, (2)Oregon State University, Hatfield Marine Science Center, Newport, United States, (3)Scripps Institution of Oceanography, La Jolla, CA, United States, (4)Univ California San Diego, La Jolla, United States
Prey and predator overlap at the edge of a mesoscale eddy: fine-scale, in-situ distributions to inform our understanding of oceanographic processes (640909)
Moritz S Schmid1, Robert Cowen1, Kelly L Robinson2, Jessica Y Luo3, Christian Briseño-Avena4,5 and Su Sponaugle6, (1)Oregon State University, Hatfield Marine Science Center, Newport, OR, United States, (2)University of Louisiana-Lafayette, Department of Biology, Lafayette, LA, United States, (3)NOAA Geophysical Fluid Dynamics Laboratory, Princeton, United States, (4)Oregon State University, Hatfield Marine Science Center, Newport, United States, (5)University of San Diego, Department of Environmental and Ocean Sciences, San Diego, CA, United States, (6)Oregon State University, Department of Integrative Biology, Corvallis, OR, United States
Automated Surveying of Phytoplankton Population Development in a Mesocosm Experiment (657397)
Joe Walker, University of California San Diego, La Jolla, United States; Scripps Institution of Oceanography, La Jolla, United States, Jules S Jaffe, Scripps Institution of Oceanography, La Jolla, CA, United States, Eric Coughlin Orenstein, Monterey Bay Aquarium Research Institute, Moss Landing, United States and Sarah Amiri, University of California Santa Barbara, Santa Barbara, CA, United States
Investigation of 3D, 4D, and hybrid automated methods to expedite high-precision coral segmentation (657927)
Hugh Runyan1, Vid Petrovic2, Nicole Pedersen1, Clinton Brook Edwards1, Stuart A Sandin1 and Falko Kuester2, (1)Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, United States, (2)California Institute for Telecommunications and Information Technology, UC San Diego, La Jolla, CA, United States
Leveraging automated image analysis tools to transform our capacity to assess status and trends on coral reefs. (640957)
Courtney Couch1,2, Ivor Williams3, Oscar Beijbom4, Thomas Oliver2, Bernardo Vargas-Angel5, Brett Schumacher6 and Rusty Eugene Brainard7, (1)Joint Institute for Marine and Atmospheric Research, Honolulu, HI, United States, (2)NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, Honolulu, HI, United States, (3)NOAA Pacific Islands Fisheries Science Center, Ecosystem Sciences Division, Honolulu, United States, (4)University of California San Diego, La Jolla, CA, United States, (5)National Oceanic and Atmospheric Administration, Ecosystem Sciences Division, Honolulu, HI, United States, (6)NOAA Pacific Islands Regional Office, Sustainable Fisheries Division, Honolulu, United States, (7)NOAA Fisheries, Pacific Islands Fisheries Science Center, Honolulu, HI, United States
Probabilistic Habitat Modeling for Benthic Surveys (657553)
Jackson Shields, University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia, Oscar Pizarro, ACFR, University Of Sydney, Australia and Stefan B Williams, The University of Sydney, Australian Centre for Field Robotics, Sydney, NSW, Australia
Artificial Intelligence and Computer Vision for Cost-Effective Benthic Habitat Characterization (657342)
Brandon Sackmann1, Eugene Revelas2, Kenia Whitehead1 and Craig Alexander Jones3, (1)Integral Consulting Inc., Olympia, WA, United States, (2)Integral Consulting Inc., United States, (3)Integral Consulting Inc., Santa Cruz, CA, United States
